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<p>In distributed cloud computing, ensuring Quality of Service (QoS) is critical for meeting the diverse and dynamic demands of modern applications. This study presents a hybrid algorithm that integrates the Multi-Objective Hybrid Coati Optimization Algorithm (MHCOA) with a Dynamic Clustering Grey Wolf Optimizer for efficient, QoS-aware task scheduling and resource allocation. Simulations were conducted using MATLAB R2023a on a system with an Intel Core i3 processor (3.5&#xa0;GHz) and 6GB RAM. The proposed method significantly outperforms traditional algorithms in key metrics. It achieves a task completion time of 12.35s, compared to 15.20s using traditional scheduling. Resource utilization improves to 85.6%, while QoS compliance reaches 94.2%. In comparative tests with MOGA and PSO, the proposed algorithm yields the shortest task completion time (12.5), highest resource utilization (95.3%), and best QoS satisfaction (97.2%). It also demonstrates superior scalability (320 tasks per node), energy efficiency (0.75&#xa0;J/task), and fairness (0.98 fairness index) with the lowest scheduling overhead (15 ms). Additionally, MHCOA’s implementation across four fog nodes demonstrates an efficiency factor of 51, indicating optimized task allocation in distributed networks. The findings confirm that the hybrid approach enhances system performance, reliability, and responsiveness in cloud environments. These results provide a strong foundation for scalable, adaptive cloud resource orchestration, offering practical benefits for future real-world deployments in dynamic and heterogeneous systems.</p>

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Enhancing cloud orchestration using hybrid scheduling for QoS-aware and energy-efficient task allocation

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Anupam Yadav,
  • Ashish Sharma

摘要

In distributed cloud computing, ensuring Quality of Service (QoS) is critical for meeting the diverse and dynamic demands of modern applications. This study presents a hybrid algorithm that integrates the Multi-Objective Hybrid Coati Optimization Algorithm (MHCOA) with a Dynamic Clustering Grey Wolf Optimizer for efficient, QoS-aware task scheduling and resource allocation. Simulations were conducted using MATLAB R2023a on a system with an Intel Core i3 processor (3.5 GHz) and 6GB RAM. The proposed method significantly outperforms traditional algorithms in key metrics. It achieves a task completion time of 12.35s, compared to 15.20s using traditional scheduling. Resource utilization improves to 85.6%, while QoS compliance reaches 94.2%. In comparative tests with MOGA and PSO, the proposed algorithm yields the shortest task completion time (12.5), highest resource utilization (95.3%), and best QoS satisfaction (97.2%). It also demonstrates superior scalability (320 tasks per node), energy efficiency (0.75 J/task), and fairness (0.98 fairness index) with the lowest scheduling overhead (15 ms). Additionally, MHCOA’s implementation across four fog nodes demonstrates an efficiency factor of 51, indicating optimized task allocation in distributed networks. The findings confirm that the hybrid approach enhances system performance, reliability, and responsiveness in cloud environments. These results provide a strong foundation for scalable, adaptive cloud resource orchestration, offering practical benefits for future real-world deployments in dynamic and heterogeneous systems.